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Future Land Use with GIS - TerrSet - CA Markov - ArcGIS

CA Markov Model Machine Learning Approach. ArcGIS Erdas QGIS used for data Preparation and TerrSet for Prediction GIS
Rating: 4.3 out of 54.3 (116 ratings)
448 students
Created by Lakhwinder Singh
Last updated 8/2019
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • You will be able to
  • Predict future expansion of urban area and generate future map.
  • Understand Advance concept of GIS and hands on
  • Advance concept In ArcGIS and Terrset Software
  • Understand Working with DEM
  • Running Advance Queries in GIS
  • Handling complex data of GIS
  • CA Markov Model
  • See Machine Learning in Action
  • Validation of Generated Results
  • Other Related task to GIS like UTM Zone, Mosaic of Digital Elevation Model

Requirements

  • You Must know Basic of GIS
  • Familiar with ArcGIS, ERDAS Just basics
  • You Must have software Terrset and ArcGIS both are NOT Open Source. You need to manage.
  • Must know how to prepare land use. This is advanced course. Otherwise first learn Landuse mapping using other course.
  • You must have two landuse with Good Accuracy

Description

In this course you will see Machine learning in Action using readymade land Change model Terrset (formerly IDRISI ) . This course used Terrset Software with CA Markov method to predict future landuse ArcGIS is used to prepare data. Erdas also used for some task. No coding is used .All software used in this course are NOT Open Source. You need to manage software. You must know to prepare landuse maps rest of things covered in this course from scratch. Future prediction of landuse depends on number of drivers/Parameters. Drives means forces which decide how the future urban area will look. It includes many drives like, old city boundary because new settlement will be constructed near to old city boundary. Roads and relief are also one of factors, because first roads near city covered by settlement. On another side how, much possibility at different location on agriculture site that can be convert to urban. Similarly, forest cover also. We also need to avoid some landuse classed like water, river, lake or reservoir never convert to urban. So, we need to setup our model in such a way so that it avoids water. After setting accuracy of learning and output accuracy also matters. We also need to modify it. In this course we have achieved learning accuracy of 42%, and 67% in two different runs. But 89% accuracy we have achieved in predicted landuse. Learning and prediction accuracy is different on computer to computer and data to data. While running you will receive more or less accuracy then this course. But focus on your output results. If Learning accuracy was 100% then it also wrong. So, see and understand each video carefully. Then run you model. You must see free preview video before enrolling this course. Because this is Expert level course.

Note: Who having IDRISI Taiga They can also follow same steps.

This course covers 90% Practical and 10% Theory.

Don’t hesitate to ask me Questions in QA Session.

Who this course is for:

  • Water Resource Engineers
  • Urban Planner
  • Land Mangement teams
  • Student of GIS Masters and Phd Level
  • Student of Remote Sensing
  • Civil Enginners
  • Remote Sensing and GIS Project Scientist

Course content

15 sections • 47 lectures • 4h 3m total length

  • Preview00:28
  • Preview00:41
  • Preview02:06
  • Preview00:53

  • Preview02:29
  • Preview01:06
  • Preview00:33
  • Understanding landuse value order
    01:34
  • Preview05:00

  • Getting Ready Our Landuse for future Input
    14:50
  • Urban Landuse Setting up for Model
    06:07
  • Disturbances Urban
    03:30

  • Downloading for Roads
    05:14
  • Preview01:57
  • Street Map Conversion
    06:33
  • Cut Vector layer to study area
    05:13
  • Road Separation from other line features in Data using Query
    04:14
  • Road distance
    04:41

  • Downloading Dem
    04:42
  • Prepare Elevation Model for use with Prediction model
    09:18
  • Process landuse with Erdas to be ready for model
    12:26
  • Process Landuse in ArcGIS (Optional)
    06:00
  • Slope Just A simple work
    01:38

  • Arrange Data for Batch Processing
    04:37

  • Adding optional road layer to main data
    11:00

  • Project setup in Terrset
    02:08
  • Tiff File conversion for Model
    02:00
  • Setting up Land Change Modeler and Image modification
    07:03
  • Estimating Spatial Trend Change probabilities for Landuse
    05:30

  • Setting up and understand transition sub model for Land change
    04:02
  • Testing power of Drivers and Sub Model setup
    05:24

  • Running the Machine Learning and MLP Model
    10:45
  • Generating future Image with Markov Chain Model
    04:00

Instructor

Lakhwinder Singh
Ph.D Research Scholar in GIS with Water Resources App
Lakhwinder Singh
  • 4.5 Instructor Rating
  • 1,044 Reviews
  • 3,119 Students
  • 6 Courses

I doing PhD at IIT Roorkee. I have wide Experience of GIS more than 12 years. I have developed my Own Models, Online Apps. Performed Automated task in GIS for faster Project management. I did many real projects for Govt. Like planing of the lake. Discharge Simulation, Soil Erosion, Sediment model development and analysis. Research is published in International Journals. You can find me on Research-gate also. I answer GIS Queries around the world with my GIS Facebook page followed by 25k and Youtube followed by 48k. and running worldwide GIS discussion group with 50k Expert of GIS around the world. Even I have a free Basic tutorial website. I also taught GIS course for Govt Engineers sponsored by Govt with real-life problems and discussion. So I have wide Experience of the teaching of GIS. I know how to teach difficult task in an Easy way.  I am providing a course at my own place to student around the world. Including Nepal, India, Ghana, Ethiopia, Zambia, South Africa, etc  At my own place I teaching full GIS course from Basic to Advanced covering 124 topics. My student required just basic knowledge of computer and they will become GIS Expert at End of Course. 

Received International Award for Best SWAT Support, from Texas A&M University, USA and USDA at Brussels, Belgium

All course 90% practical 10% theory 

My training Method of Different than All others. More user friendly from practical Examples. I know how our brain learns scientifically.

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